try use py to auto create dataset

vi_backbones.py

@@ -0,0 +1,103 @@
+import os
+import re
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+# import random
+import datasets
+
+
+_DBNAME = os.path.basename(__file__).split('.')[0]
+
+_HOMEPAGE = "https://huggingface.co/datasets/george-chou/" + _DBNAME
+
+_URL = 'https://pytorch.org/vision/main/_modules/'
+
+_TYPES = pd.read_csv(_HOMEPAGE + '/resolve/main/data/backbone.csv',
+                     index_col='ver').to_dict()['type']
+
+_NAMES = sorted(list(set(_TYPES.values())))
+
+
+class vi_backbones(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "ver": datasets.Value("string"),
+                    "type": datasets.Value("string"),
+                    # "type": datasets.features.ClassLabel(names=_NAMES),
+                    "input_size": datasets.Value("int32"),
+                    "input_size": datasets.Value("int32"),
+                    "url": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=("ver", "type"),
+            homepage=_HOMEPAGE,
+            license="mit"
+        )
+
+    def _parse_url(self, url):
+        response = requests.get(url)
+        html = response.text
+        return BeautifulSoup(html, 'html.parser')
+
+    def _generate_dataset(self, url):
+        torch_page = self._parse_url(url)
+        article = torch_page.find('article', {'id': 'pytorch-article'})
+        ul = article.find('ul').find('ul')
+        dataset = []
+        for li in ul.find_all('li'):
+            name = str(li.text)
+            if name.__contains__('torchvision.models.') and len(name.split('.')) == 3:
+                if name.__contains__('_api') or name.__contains__('feature_extraction'):
+                    continue
+                href = li.find('a').get('href')
+                model_page = self._parse_url(url + href)
+                divs = model_page.select('div.viewcode-block')
+                for div in divs:
+                    div_id = str(div['id'])
+                    if div_id.__contains__('_Weights'):
+                        m_ver = div_id.split('_Weight')[0].lower()
+                        m_type = re.search('[a-zA-Z]+', m_ver).group(0)
+                        input_size = int(
+                            div.find('span', {'class': 'mi'}).text)
+                        m_url = str(div.find('span', {'class': 's2'}).text)
+                        m_dict = {
+                            'ver': m_ver,
+                            'type': m_type,
+                            'input_size': input_size,
+                            'url': m_url
+                        }
+                        print('Adding ' + m_ver)
+                        dataset.append(m_dict)
+        return dataset
+
+    def _split_generators(self, dl_manager):
+        dataset = self._generate_dataset(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name="IMAGENET1K_V1",
+                gen_kwargs={
+                    "files": dataset,
+                },
+            ),
+            datasets.SplitGenerator(
+                name="IMAGENET1K_V2",
+                gen_kwargs={
+                    "files": dataset,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, model in enumerate(files):
+            yield i, {
+                "ver": model['ver'],
+                "type": model['type'],
+                "input_size": model['input_size'],
+                "output_size": 9216,
+                "url": model['url'],
+            }